Prediction of GC retention values under various column temperature conditions from temperature programmed data

A numerical approach has been developed for the correlation of retention limes (total retention lime) with temperature in gas chromatography, which allows the calculation of retention parameters including retention index from data acquired under two or more different temperature program conditions. By using this procedure the optimization of temperature condition can be further achieved, especially when a temperature-programmed run is the most suitable mode in the preliminary development of an analytical method for the analysis of an unknown sample.

Retention time prediction and protein identification

Proteins are commonly identified through enzymatic digestion and generation of short sequence tags or fingerprints of peptide masses by mass spectrometry. Separation methods, such as liquid chromatography and electrophoresis, are often used to fractionate complex protein or peptide mixtures and these separations also provide information on the different species, such as molecular weight and isoelectric point from electrophoresis and hydrophobicity in reversed-phase chromatography. These are also properties that can be predicted from amino acid sequences derived from genomic sequences and used in protein identification. This chapter reviews recently introduced methods based on retention time prediction to extract information from chromatographic separations and the applications to protein identification in organisms with small and large genomes. Novel data on retention time prediction of posttranslationally modified peptides is also presented.

Prediction of moisture, calorific value, ash and carbon content of two dedicated bioenergy crops using near-infrared spectroscopy

The potential of near infrared spectroscopy in conjunction with partial least squares regression to predict Miscanthus xgiganteus and short rotation coppice willow quality indices was examined. Moisture, calorific value, ash and carbon content were predicted with a root mean square error of cross validation of 0.90% (R2 = 0.99), 0.13 MJ/kg (R2 = 0.99), 0.42% (R2 = 0.58), and 0.57% (R2 = 0.88), respectively. The moisture and calorific value prediction models had excellent accuracy while the carbon and ash models were fair and poor, respectively. The results indicate that near infrared spectroscopy has the potential to predict quality indices of dedicated energy crops, however the models must be further validated on a wider range of samples prior to implementation. The utilization of such models would assist in the optimal use of the feedstock based on its biomass properties.

Likelihood prediction for generalized linear mixed models under covariate uncertainty

This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable generalized linear model, it has been shown that in complicated cases LP produces better results than already know methods.

A comparison between highly depithed and conventionally depithed bagasse pulp

Sugarcane bagasse pulp normally has high dewatering resistance and poor strength properties. In a previous study it was shown that highly depithed bagasse chemical pulp has excellent dewatering properties which may improve the production rate of bagasse based tissue, paper and board. In this study pulp properties of this highly depithed bagasse pulp were tested and compared favourably with regular depithed bagasse pulp. In addition to better dewatering rates, the pulp yield, tear strength and water retention value seemingly improved. Whilst a slight reduction in burst, tensile and short-span compression strengths occurred, they were still comparable to values reported for a regular bagasse pulp.

Sustainable Road Management in Texas : network-level flexible pavement structural condition analysis using data mining techniques

The research team recognized the value of network-level Falling Weight Deflectometer (FWD) testing to evaluate the structural condition trends of flexible pavements. However, practical limitations due to the cost of testing, traffic control and safety concerns and the ability to test a large network may discourage some agencies from conducting the network-level FWD testing. For this reason, the surrogate measure of the Structural Condition Index (SCI) is suggested for use. The main purpose of the research presented in this paper is to investigate data mining strategies and to develop a prediction method of the structural condition trends for network-level applications which does not require FWD testing. The research team first evaluated the existing and historical pavement condition, distress, ride, traffic and other data attributes in the Texas Department of Transportation (TxDOT) Pavement Maintenance Information System (PMIS), applied data mining strategies to the data, discovered useful patterns and knowledge for SCI value prediction, and finally provided a reasonable measure of pavement structural condition which is correlated to the SCI. To evaluate the performance of the developed prediction approach, a case study was conducted using the SCI data calculated from the FWD data collected on flexible pavements over a 5-year period (2005 – 09) from 354 PMIS sections representing 37 pavement sections on the Texas highway system. The preliminary study results showed that the proposed approach can be used as a supportive pavement structural index in the event when FWD deflection data is not available and help pavement managers identify the timing and appropriate treatment level of preventive maintenance activities.

RST: Reuse through Speculation on Traces

In this thesis, we present a novel approach to combine both reuse and prediction of dynamic sequences of instructions called Reuse through Speculation on Traces (RST). Our technique allows the dynamic identification of instruction traces that are redundant or predictable, and the reuse (speculative or not) of these traces. RST addresses the issue, present on Dynamic Trace Memoization (DTM), of traces not being reused because some of their inputs are not ready for the reuse test. These traces were measured to be 69% of all reusable traces in previous studies. One of the main advantages of RST over just combining a value prediction technique with an unrelated reuse technique is that RST does not require extra tables to store the values to be predicted. Applying reuse and value prediction in unrelated mechanisms but at the same time may require a prohibitive amount of storage in tables. In RST, the values are already stored in the Trace Memoization Table, and there is no extra cost in reading them if compared with a non-speculative trace reuse technique. . The input context of each trace (the input values of all instructions in the trace) already stores the values for the reuse test, which may also be used for prediction. Our main contributions include: (i) a speculative trace reuse framework that can be adapted to different processor architectures; (ii) specification of the modifications in a superscalar, superpipelined processor in order to implement our mechanism; (iii) study of implementation issues related to this architecture; (iv) study of the performance limits of our technique; (v) a performance study of a realistic, constrained implementation of RST; and (vi) simulation tools that can be used in other studies which represent a superscalar, superpipelined processor in detail. In a constrained architecture with realistic confidence, our RST technique is able to achieve average speedups (harmonic means) of 1.29 over the baseline architecture without reuse and 1.09 over a non-speculative trace reuse technique (DTM).

Comparações entre os valores genéticos para características de crescimento de bovinos da raça Nelore preditos com modelos de dimensão finita ou infinita

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Influência da densidade básica da madeira de clones de Eucalyptus grandis x Eucalyptus urophylla na qualidade da polpa branqueada.

The study analyzed the wood basic density effect in two Eucalyptus grandis x Eucalyptus urophylla hybrid clones (440 kg/m(3) e 508 kg/m(3)) on bleached pulp quality (fiber dimensions and physical-mechanical properties). The woods performance on pulping, bleaching and beating results were analyzed. The Kraft pulping was carried out in forced circulation digester in order to obtain 17 +/- 1 kappa number targets. The pulps were bleached to 90 +/- 1 using delignification oxygen and D0EOPD1 bleaching sequence. Bleached pulp of low basic density clone showed, significantly, lowest revolutions number in the PFI mill to reach tensile index of 70 N.m/g, low Schopper Riegler degree and generated sheets with higher values to bulk and opacity. These characteristics and properties allow concluding that bleached pulp of low basic density clone was the most indicated to produce printing and writing sheets. The bleached pulp of high basic density clone showed higher values of bulk and capillarity Klemm and lower water retention value when analyzed without beating. The bleached pulp of high basic density clone showed more favorable characteristics to the production of tissue papers.

Implementation of DNA markers to produce genomically - Enhanced EPDs in nellore cattle

Background: The sequencing and publication of the cattle genome and the identification of single nucleotide polymorphism (SNP) molecular markers have provided new tools for animal genetic evaluation and genomic-enhanced selection. These new tools aim to increase the accuracy and scope of selection while decreasing generation interval. The objective of this study was to evaluate the enhancement of accuracy caused by the use of genomic information (Clarifide® - Pfizer) on genetic evaluation of Brazilian Nellore cattle. Review: The application of genome-wide association studies (GWAS) is recognized as one of the most practical approaches to modern genetic improvement. Genomic selection is perhaps most suited to the improvement of traits with low heritability in zebu cattle. The primary interest in livestock genomics has been to estimate the effects of all the markers on the chip, conduct cross-validation to determine accuracy, and apply the resulting information in GWAS either alone [9] or in combination with bull test and pedigree-based genetic evaluation data. The cost of SNP50K genotyping however limits the commercial application of GWAS based on all the SNPs on the chip. However, reasonable predictability and accuracy can be achieved in GWAS by using an assay that contains an optimally selected predictive subset of markers, as opposed to all the SNPs on the chip. The best way to integrate genomic information into genetic improvement programs is to have it included in traditional genetic evaluations. This approach combines traditional expected progeny differences based on phenotype and pedigree with the genomic breeding values based on the markers. Including the different sources of information into a multiple trait genetic evaluation model, for within breed dairy cattle selection, is working with excellent results. However, given the wide genetic diversity of zebu breeds, the high-density panel used for genomic selection in dairy cattle (Ilumina Bovine SNP50 array) appears insufficient for across-breed genomic predictions and selection in beef cattle. Today there is only one breed-specific targeted SNP panel and genomic predictions developed using animals across the entire population of the Nellore breed (www.pfizersaudeanimal.com), which enables genomically - enhanced selection. Genomic profiles are a way to enhance our current selection tools to achieve more accurate predictions for younger animals. Material and Methods: We analyzed the age at first calving (AFC), accumulated productivity (ACP), stayability (STAY) and heifer pregnancy at 30 months (HP30) in Nellore cattle fitting two different animal models; 1) a traditional single trait model, and 2) a two-trait model where the genomic breeding value or molecular value prediction (MVP) was included as a correlated trait. All mixed model analyses were performed using the statistical software ASREML 3.0. Results: Genetic correlation estimates between AFC, ACP, STAY, HP30 and respective MVPs ranged from 0.29 to 0.46. Results also showed an increase of 56%, 36%, 62% and 19% in estimated accuracy of AFC, ACP, STAY and HP30 when MVP information was included in the animal model. Conclusion: Depending upon the trait, integration of MVP information into genetic evaluation resulted in increased accuracy of 19% to 62% as compared to accuracy from traditional genetic evaluation. GE-EPD will be an effective tool to enable faster genetic improvement through more dependable selection of young animals.

Grão de girassol ou gordura protegida na dieta com alto concentrado e o ganho compensatório para cordeiros

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Predicting the solvent accessibility of transmembrane residues from protein sequence

In this study, we propose a novel method to predict the solvent accessible surface areas of transmembrane residues. For both transmembrane alpha-helix and beta-barrel residues, the correlation coefficients between the predicted and observed accessible surface areas are around 0.65. On the basis of predicted accessible surface areas, residues exposed to the lipid environment or buried inside a protein can be identified by using certain cutoff thresholds. We have extensively examined our approach based on different definitions of accessible surface areas and a variety of sets of control parameters. Given that experimentally determining the structures of membrane proteins is very difficult and membrane proteins are actually abundant in nature, our approach is useful for theoretically modeling membrane protein tertiary structures, particularly for modeling the assembly of transmembrane domains. This approach can be used to annotate the membrane proteins in proteomes to provide extra structural and functional information.